Physical Layer Authentication Based on Transformer

Ai, Xin; Yue, Qingqing; Li, Hongchen; Li, Wenlong; Tu, Shanshan; Rehman, Sadaqat Ur

Abstract

With the rapid proliferation of wireless devices, effectively authenticating legitimate users has become a pivotal challenge in wireless communication. Amongst various approaches, physical layer authentication technology based on deep learning has garnered substantial attention from numerous researchers. In this paper, we propose a scheme for implementing physical layer authentication based on the Swin Transformer, utilizing Channel State Information (CSI) to distinguish between legitimate and illegitimate nodes in industrial network systems. In contrast to traditional physical layer authentication methods based on thresholds, the method proposed in this paper eschews the use of thresholds to achieve authentication. Moreover, compared to other methods based on deep neural networks, the introduction of attention mechanisms enables superior learning of wireless channel state features, enhancing model accuracy and reducing computational complexity. The efficacy of this scheme is validated through channel probing results in typical industrial wireless environments provided by the National Institute of Standards and Technology (NIST), which will facilitate the application of deep learning technology to industrial wireless network systems to enhance their security.

Citation

Ai, X., Yue, Q., Li, H., Li, W., Tu, S., & Rehman, S. U. (2023). Physical Layer Authentication Based on Transformer. In ICCNS '23: Proceedings of the 2023 13th International Conference on Communication and Network Security (203–208). https://doi.org/10.1145/3638782.3638813

• Conference Name: 13th International Conference on Communication and Network Security
• Conference Location: Fuzhou, China
• Start Date: Dec 1, 2023
• End Date: Dec 3, 2023
• Acceptance Date: Aug 10, 2023
• Online Publication Date: Apr 18, 2024
• Publication Date: Dec 6, 2023
• Deposit Date: May 8, 2024
• Publisher: Association for Computing Machinery (ACM)
• Pages: 203–208
• Book Title: ICCNS '23: Proceedings of the 2023 13th International Conference on Communication and Network Security
• DOI: https://doi.org/10.1145/3638782.3638813